In some methods of medical diagnosis, a health professional may review a set or a stream of images of a body organ in order to identify pathological symptoms in this body organ.
In some cases the set of images is long and monotonous. For example, in some cases the set of images is captured along a path within the living body, and may include tens of thousands of images. Many of the images, and sometimes the vast majority of the images, may be of a healthy tissue and/or redundant for medical diagnosis. Therefore, it may be tiresome and time consuming to review the whole set of images in order to find suspected pathological symptoms, usually in a minority of the images.
Therefore, it may be desirable to enable a health professional to view a shortened set of images which excludes images that may be uninformative for medical diagnosis, such as images of healthy body tissue and/or redundant images.
Computer-Aided Diagnostics (CAD) may facilitate a more efficient review of a set of images by a health professional, by using computerized classification tools (classifiers) that enable classification of images, for example by computerized image processing and analysis methods. A classification algorithm may be obtained by machine learning based on a training set of images. The training set of images may include pre-labeled images, labeled by a professional as images of either healthy or suspected pathological tissue. A computer processor may analyze attributes of the images in the training set, and based on the attributes and usually some predetermined criterions may build an algorithm for classification of images as either healthy or suspected pathological tissue. Known computerized classification tools may receive a set of unlabeled images and classify the images based on the algorithm that was built based on the training set of labeled images. Typically, an image is analyzed by image processing in order to identify attributes that may imply a suspected pathological symptom.
Once the attributes of an image are identified, the algorithm may be used for classifying the image.
Accordingly, once the classification algorithm is built, the known CAD tools use the same classification algorithm for classifying input images from various input image streams, which may be received from different patients and different image stream segments related to the same patient's body. The classification algorithm in these tools is constant once it is built. Since the distribution of the image data items in the data space (to which the data items are mapped according to their attributes) is typically changing from patient to patient and also from segment to segment within the same image stream related to the same patient, the constant classification algorithm may not be optimal for all possible input images. Additionally, since CAD tools seek to avoid false-negative kind of errors (e.g., classification of an imaged situation as healthy by error) in all possible input images, the thresholds of the classification algorithm are set to classify as unhealthy even the most extreme possible cases with very low probability to be unhealthy.
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